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Proximal Sensing in Precision Agriculture
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Proximal Sensing in Precision Agriculture

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Smart Agriculture".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 5772

Special Issue Editor

Dr. Somsubhra Chakraborty

E-Mail Website
Guest Editor
Indian Institute of Technology Kharagpur, Kharagpur, India
Interests: proximal soil sensors; precision agriculture; smartphone image-based soil characterization; portable X-ray fluorescence spectrometer; diffuse reflectance spectroscopy; cost-effective optical soil sensors

Special Issue Information

Dear Colleagues,

There is a need to develop a mechanical framework in precision agriculture for rapidly predicting soil and crop properties that can handle the ever-increasing demand for soil and crop characterization, especially in resource-poor conditions. This Special Issue promotes the innovative outcomes resulting from the research in the field of proximal sensing in precision agriculture. Proximal soil sensing refers to a group of technologies that use a sensor in proximity to the soil such as diffuse reflectance spectroscopy, portable X-ray fluorescence spectroscopy, LIBS, Nix, digital camera, gamma spectrometry, electromagnetic induction, GPR, TDR, ISEFET, ion selective electrodes, etc. Consequently, the proximal soil sensors directly or indirectly measure the targeted soil property. Moreover, crop properties or damage can be assessed using drone images and tractor-mounted or handheld proximal sensors. This Special Issue embraces every aspect of proximal soil and crop sensing and welcomes research papers that can potentially advance the current scientific knowledge of rapid soil and crop characterization.

Dr. Somsubhra Chakraborty
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • soil sensor
  • crop
  • image analysis
  • PXRF
  • DRS
  • AIML

Published Papers (3 papers)

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Research

14 pages, 4199 KiB  
Article
Using a Non-Contact Sensor to Delineate Management Zones in Vineyards and Validation with the Rasch Model
by Francisco J. Moral, Francisco J. Rebollo and João Serrano
Sensors 2023, 23(22), 9183; https://doi.org/10.3390/s23229183 - 14 Nov 2023
Cited by 1 | Viewed by 1303
Abstract
The production of high-quality wines is one of the primary goals of modern oenology. In this regard, it is known that the potential quality of a wine begins to be determined in the vineyard, where the quality of the grape, initially, and later [...] Read more.
The production of high-quality wines is one of the primary goals of modern oenology. In this regard, it is known that the potential quality of a wine begins to be determined in the vineyard, where the quality of the grape, initially, and later that of the wine, will be influenced by the soil properties. Given the spatial variability of the fundamental soil properties related to the potential grape production, such as texture, soil organic matter content, or cation exchange capacity, it seems that a uniform management of a vineyard is not the most optimal way to achieve higher grape quality. In this sense, the delineation of zones with similar soil characteristics to implement site-specific management is essential, reinforcing the interest in incorporating technologies and methods to determine these homogeneous zones. A case study was conducted in a 3.3 ha vineyard located near Évora, south of Portugal. A non-contact sensor (DUALEM 1S) was used to measure soil apparent electrical conductivity (ECa) in the vineyard, and later, a kriged ECa map was generated. ECa and elevation maps were utilised to delineate homogeneous zones (management zones, MZs) in the field through a clustering process. MZs were validated using some soil properties (texture; pH; organic matter—OM; phosphorous—P2O5; potassium—K2O; the sum of the exchange bases—SEB; and cation exchange capacity—CEC), which were determined from 20 soil samples taken in the different MZs. Validation was also performed using Rasch measures, which were defined based on the formulation of the objective and probabilistic Rasch model, integrating the information from the aforementioned soil properties at each sampling location. The comparison of the MZs was more evident with the use of the Rasch model, as only one value was to be employed in each MZ. Finally, an additional validation was conducted using a vegetation index to consider the plant response, which was different in each MZ. The use of a non-contact sensor to measure ECa constitutes an efficient technological tool for implementing site-specific management in viticulture, which allows for the improvement of decision-making processes by considering the inherent spatial variability of the soil. Full article
(This article belongs to the Special Issue Proximal Sensing in Precision Agriculture)
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16 pages, 2935 KiB  
Article
A Smartphone-Enabled Imaging Device for Chromotropic Acid-Based Measurement of Nitrate in Soil Samples
by Veerabhadrappa Lavanya, Anshuman Nayak, Partha Deb Roy, Shubhadip Dasgupta, Subhadip Dey, Bin Li, David C. Weindorf and Somsubhra Chakraborty
Sensors 2023, 23(17), 7345; https://doi.org/10.3390/s23177345 - 23 Aug 2023
Viewed by 2142
Abstract
In this study, a novel chromotropic acid-based color development method was proposed for quick estimation of soil nitrate (NO3−). The method utilized a 3D printed device integrated with the rear-end camera of a smartphone and a stand-alone application called SMART [...] Read more.
In this study, a novel chromotropic acid-based color development method was proposed for quick estimation of soil nitrate (NO3−). The method utilized a 3D printed device integrated with the rear-end camera of a smartphone and a stand-alone application called SMART NP. By analyzing the mean Value (V) component of the sample’s image, the SMART NP provides instant predictions of soil NO3− levels. The limit of detection was calculated as 0.1 mg L−1 with a sensitivity of 0.26 mg L−1. The device showed a % bias of 0.9% and a precision of 1.95%, indicating its reliability. Additionally, the device-predicted soil NO3− data, combined with kriging interpolation, showcased spatial variability in soil NO3− levels at the regional level. The study employed a Gaussian model of variogram for kriging, and the high Nugget/Sill ratio indicated low spatial autocorrelation, emphasizing the impact of management factors on the spatial distribution of soil NO3− content in the study area. Overall, the imaging device, along with geostatistical interpolation, provided a comprehensive solution for the rapid assessment of spatial variability in soil NO3−content. Full article
(This article belongs to the Special Issue Proximal Sensing in Precision Agriculture)
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14 pages, 25531 KiB  
Article
Development of a Target-to-Sensor Mode Multispectral Imaging Device for High-Throughput and High-Precision Touch-Based Leaf-Scale Soybean Phenotyping
by Xuan Li, Ziling Chen, Xing Wei, Tianzhang Zhao and Jian Jin
Sensors 2023, 23(7), 3756; https://doi.org/10.3390/s23073756 - 05 Apr 2023
Cited by 1 | Viewed by 1665
Abstract
Image-based spectroscopy phenotyping is a rapidly growing field that investigates how genotype, environment and management interact using remote or proximal sensing systems to capture images of a plant under multiple wavelengths of light. While remote sensing techniques have proven effective in crop phenotyping, [...] Read more.
Image-based spectroscopy phenotyping is a rapidly growing field that investigates how genotype, environment and management interact using remote or proximal sensing systems to capture images of a plant under multiple wavelengths of light. While remote sensing techniques have proven effective in crop phenotyping, they can be subject to various noise sources, such as varying lighting conditions and plant physiological status, including leaf orientation. Moreover, current proximal leaf-scale imaging devices require the sensors to accommodate the state of the samples during imaging which induced extra time and labor cost. Therefore, this study developed a proximal multispectral imaging device that can actively attract the leaf to the sensing area (target-to-sensor mode) for high-precision and high-throughput leaf-scale phenotyping. To increase the throughput and to optimize imaging results, this device innovatively uses active airflow to reposition and flatten the soybean leaf. This novel mechanism redefines the traditional sensor-to-target mode and has relieved the device operator from the labor of capturing and holding the leaf, resulting in a five-fold increase in imaging speed compared to conventional proximal whole leaf imaging device. Besides, this device uses artificial lights to create stable and consistent lighting conditions to further improve the quality of the images. Furthermore, the touch-based imaging device takes full advantage of proximal sensing by providing ultra-high spatial resolution and quality of each pixel by blocking the noises induced by ambient lighting variances. The images captured by this device have been tested in the field and proven effective. Specifically, it has successfully identified nitrogen deficiency treatment at an earlier stage than a typical remote sensing system. The p-value of the data collected by the device (p = 0.008) is significantly lower than that of a remote sensing system (p = 0.239). Full article
(This article belongs to the Special Issue Proximal Sensing in Precision Agriculture)
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